#注释1
import tensorflow as tf
import random
import os
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = 2
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #改错 增加引号

#注释2
from tensorflow.examples.tutorials.mnist import input_data
#注释3
tf.set_random_seed(777)

import sys
import os
path = r'../../../../large_data/DL1/mnist'
if not os.path.exists(path):
    print('[[[ DIR WRONG! ]]]', file=sys.stderr)
    sys.exit(0)
mnist = input_data.read_data_sets(path, one_hot=True)

#注释4
learning_rate = 0.001
#注释5
training_epochs = 15
#注释6
batch_size = 100

# TB_SUMMARY_DIR = ' '
TB_SUMMARY_DIR = 'logs/t1'   #改错指定目录，注意目录最后不能有空格


#注释7
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

#注释8
x_image = tf.reshape(X, [-1, 28, 28, 1])
#注释9
tf.summary.image('input', x_image, 3)


#注释10
keep_prob = tf.placeholder(tf.float32)

#注释11
with tf.variable_scope('layer1') as scope: #改错 增加冒号
    W1 = tf.get_variable("W", shape=[784, 512],
                         initializer=tf.contrib.layers.xavier_initializer())
    b1 = tf.Variable(tf.random_normal([512]) )  #改错增加右括号
    ##注释12
    L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
    ##注释13
    L1 = tf.nn.dropout(L1, keep_prob=keep_prob)
    # 注释14
    tf.summary.histogram("X", X)
    tf.summary.histogram("weights", W1)
    tf.summary.histogram("bias", b1)
    tf.summary.histogram("layer", L1)

with tf.variable_scope('layer2') as scope: #改错 增加冒号
    W2 = tf.get_variable("W", shape=[512, 512],
                         initializer=tf.contrib.layers.xavier_initializer())
    b2 = tf.Variable(tf.random_normal([512]))
    L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
    L2 = tf.nn.dropout(L2, keep_prob=keep_prob)

    tf.summary.histogram("weights", W2)
    tf.summary.histogram("bias", b2)
    tf.summary.histogram("layer", L2)

with tf.variable_scope('layer3') as scope:
    W3 = tf.get_variable("W", shape=[512, 512],
                         initializer=tf.contrib.layers.xavier_initializer())
    b3 = tf.Variable(tf.random_normal([512]))
    # L3 = tf.relu(tf.matmul(L2, W3) + b3)
    L3 = tf.nn.relu(tf.matmul(L2, W3) + b3)#改错增加nn
    # L3 = tf.dropout(L3, keep_prob=keep_prob)
    L3 = tf.nn.dropout(L3, keep_prob=keep_prob) #改错增加 nn

    tf.summary.histogram("weights", W3)
    tf.summary.histogram("bias", b3)
    tf.summary.histogram("layer", L3)

with tf.variable_scope('layer4') as scope:
    W4 = tf.get_variable("W", shape=[512, 512],
                         initializer=tf.contrib.layers.xavier_initializer())
    b4 = tf.Variable(tf.random_normal([512]))
    L4 = tf.nn.relu(tf.matmul(L3, W4) + b4)
    L4 = tf.nn.dropout(L4, keep_prob=keep_prob)

    tf.summary.histogram("weights", W4)
    tf.summary.histogram("bias", b4)
    tf.summary.histogram("layer", L4)

with tf.variable_scope('layer5') as scope:
    W5 = tf.get_variable("W", shape=[512, 10],
                         initializer=tf.contrib.layers.xavier_initializer())
    b5 = tf.Variable(tf.random_normal([10]))
    hypothesis = tf.matmul(L4, W5) + b5

    tf.summary.histogram("weights", W5)
    tf.summary.histogram("bias", b5)
    tf.summary.histogram("hypothesis", hypothesis)


#注释15
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=hypothesis, labels=Y))
#注释16
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
#注释17
tf.summary.scalar("loss", cost)

#注释22
correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
#注释23
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#注释18
summary = tf.summary.merge_all()

#注释19
# sess = tf.Session()
sess = tf.Session() #改错 去掉 #
sess.run(tf.global_variables_initializer())

#注释20
# writer = summary.FileWriter(TB_SUMMARY_DIR)
writer = tf.summary.FileWriter(TB_SUMMARY_DIR) #改错 增加 tf
writer.add_graph(sess.graph)
global_step = 0

print('Start learning!')

#注释21
for epoch in range(training_epochs):
    avg_cost = 0
    avg_acc = 0
    total_batch = int(mnist.train.num_examples / batch_size)
    group = total_batch // 20
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7}
        s, _, costv, accv = sess.run([summary, optimizer, cost, accuracy], feed_dict=feed_dict)
        writer.add_summary(s, global_step=global_step)
        global_step += 1

        avg_cost += costv / total_batch
        avg_acc += accv / total_batch
        if i % group == 0:
            print(f'Epoch#{epoch+1}:batch#{i+1} cost = {costv}, acc = {accv}')

    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost), 'acc =', f'{avg_acc:.9f}')

print('Learning Finished!')

print('Accuracy:', sess.run(accuracy, feed_dict={
      X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

##注释24
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
    # tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]; keep_prob: 1}))
tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1})) #改错分号改为逗号

